Pandas Isin (): the filtering method

The Python Pandas function DataFrame.isin() is used to check quickly and efficiently If certain values ​​are present in a dataframa. This function is particularly useful when you want to check several values ​​at the same time.

The syntax of the function isin() pandas

Pandas isin() takes only one parameter. Consequently, the basic syntax of the function is very simple and presents itself as follows:

DataFrame.isin(values)

The parameter values Maybe a Python list, a python dictionary or another data And contains the values ​​to be sought in the original dataframe.

How to use the function isin() of pandas?

The possibilities of using isin() are multiple: using this function, you can not only check the presence of values, but also filter your dataaframa.

Check the presence of values ​​in a column

We will now examine a dataframe containing information on different people and their city of residence.

import pandas as pd
# Créer un exemple de DataFrame
data = {
    'Nom' : ['Alice', 'Bob', 'Charlie', 'David'],
    'Ville' : ['New York', 'Los Angeles', 'Chicago', 'Houston']
}
df = pd.DataFrame(data)
print(df)

Dataframa presents itself as follows:

Nom      Ville
0   Alice     New York
1     Bob  Los Angeles
2  Charlie     Chicago
3   David    Houston

We then want to check with the method isin() of pandas if certain cities are present in the corresponding column. To do this, we create a list called « cities_to_check » (translated by « cities to be checked »), then we perform the function on the DataFrame column called « City »:

# Villes à vérifier
cities_to_check = ['Chicago', 'Houston', 'Miami']
# Application de la méthode isin()
result = df['Ville'].isin(cities_to_check)
print(result)

The result is a series of Boolean values ​​which indicate if the city in question is present in the list of cities to be checked:

0 False
1 False
2 True
3 True
Nom : ville, dtype: bool

Filter values ​​in the dataframa with isin()

THE isin() of pandas can also be used to filter the dataframa in order to keep only the lines containing the cities present in cities_to_check.

# Filtrer le DataFrame en se basant sur isin()
filtered_df = df[df['Ville'].isin(cities_to_check)]
print(filtered_df)

The result is a dataframa that contains only lines with cities that are also present in the comparison list cities_to_check ::

Nom    Ville
2  Charlie   Chicago
3    David   Houston

Check several columns

To perform more complex filtering operations, isin() De Pandas can also be used with Python dictionaries. The following example shows how to work with a dictionary to simultaneously check several columns of a dataframa. To do this, the original dataframa is extended from a column and isin() is then used:

# Créer un exemple de DataFrame
data = {
    'Nom' : ['Alice', 'Bob', 'Charlie', 'David'],
    'Ville' : ['New York', 'Los Angeles', 'Chicago', 'Houston'],
    'Âge' : [25, 30, 35, 40]
}
df = pd.DataFrame(data)
# Dictionnaire des valeurs à vérifier
values_to_check = {
    'Ville' : ['Chicago', 'Houston'],
    'Âge' : [30, 40]
}
# Application de isin() avec un dictionnaire
result = df.isin(values_to_check)
print(result)

In this case, the appeal of function of isin() Returns a dataframe with Boolean values ​​which indicate whether the conditions are met in the respective columns:

Nom    Ville    Âge
0  False   False  False
1  False   False   True
2  False    True  False
3  False    True   True